Many data sets coming from the world around us—transportation systems, human proximity, interactions on social media, etc.—take the form of networks [1]}, [2]}. One of network science's main objectives is to simplify complex, large-scale data sets to highlight important structures—like a map simplifies the geography of a country. There are several different perspectives one can take on how to map out a network. Perhaps the most popular direction is to detect mesoscopic structures, such as community structure [3]}. This is somewhat analogous to charting the cities of a country. In this paper, however, we start from the orthogonal problem of identifying the country's highways, i.e., the network backbone structure. Mapping network backbones is intimately connected to another one of network science's aims—to explain and predict dynamic processes on the network [2]}. To understand the flow of information or disease on a network, knowing the highway structure is more helpful than knowing the cities.
